lmrob.fit: MM-type estimator for regression
Description
Compute MM-type estimators of regression: An S-estimator is
used as starting value, and an M-estimator with fixed scale and
redescending psi-function is used from there. Optionally a D-step
(Design Adaptive Scale estimate) as well as a second M-step is
calculated.Usage
lmrob.fit(x, y, control, init = NULL, mf = NULL)
Arguments
x
design matrix ($n \times p$) typically including a
column of 1s for the intercept.
y
numeric response vector (of length $n$).
control
a list of control parameters as returned
by lmrob.control, used for both the initial S-estimate
and the subsequent M- and D-estimates. init
optional list of initial estimates. See
Details. Value
- A list with components
- fitted.values$X \beta$, i.e.,
X %*% coefficients. - residualsthe raw residuals,
y - fitted.values - rweightsrobustness weights derived from the final M-estimator
residuals (even when not converged).
- rank
- degree.freedom
n - rank - coefficientsestimated regression coefficient vector
- scalethe robustly estimated error standard deviation
- covvariance-covariance matrix of
coefficients, if the
RWLS iterations have converged - control
- iter
- convergedlogical indicating if the RWLS iterations have converged.
- init.Sthe whole initial S-estimator result, including its own
converged flag, see lmrob.S (only for MM-estimates). - initA similar list that contains the results of intermediate
estimates (not for MM-estimates).
Details
This function is the basic fitting function for MM-type estimation,
called by lmrob and typically not to be used on its own. If given, init must be a list of initial estimates containing
at least the initial coefficients and scale as coefficients and
scale. Otherwise it calls lmrob.S(..) and uses it
as initial estimator.